A common problem in photography is image blur, which can be caused by combinations of camera shake during long exposure times, subject movement, the use of large apertures in low light settings, or limited camera resolution. Regardless of the cause, image blur is usually undesirable.
With the advent of digital photography, it has become possible to reduce or correct the blur in an image. FIG. 1 shows a blurred image subjected to a deblurring process to produce a deblurred image 102. Various approaches have been used to find a deblurred image such as image 102. Some have tried modifying how images are captured. Some have used the added information of multiple images to reduce blur. Up-sampling algorithms have been used to reduce the blur from limited camera resolution. Blur kernels determined from a single image have also been used. Non-blind deconvolution has also been explored but with limited success.
Non-blind image deconvolution involves recovering a sharp image from an input image corrupted by blurring and noise, where the blurring is a product of convolution the true (unblurred) image with a known kernel at a known noise level. Previous deconvolution approaches to deblurring have often been limited to special applications, are often not effective with arbitrary images, and sometimes generate unwanted artifacts such as ringing. Some have used image priors derived from natural image statistics. Others have used graph cuts to reduce over-smoothing. Deconvolution with multiple blurs and energy minimization have also been used.
Techniques described below relate to efficiently and reliably deblurring images using deconvolution.